class_docstr: Abstract class for parametrized probability distributions.
docstr: Samples a point from the distribution.
  
  Args:
    inputs (jnp.ndarray): Distribution inputs. Shape is subclass-specific.
      Broadcasts along the first dimensions. For example, in the categorical
      distribution parameter shape is (C,), where C is the number of
      categories. If (B, C) is passed, the object will represent a batch of B
      categorical distributions with different parameters.
    temperature: sampling temperature; 1.0 is default, at 0.0 chooses
      the most probable (preferred) action.
  
  Returns:
    Sampled point of shape dependent on the subclass and on the shape of
    inputs.
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code: def sample(self, inputs, temperature=1.0):
      """Samples a point from the distribution.
  
      Args:
        inputs (jnp.ndarray): Distribution inputs. Shape is subclass-specific.
          Broadcasts along the first dimensions. For example, in the categorical
          distribution parameter shape is (C,), where C is the number of
          categories. If (B, C) is passed, the object will represent a batch of B
          categorical distributions with different parameters.
        temperature: sampling temperature; 1.0 is default, at 0.0 chooses
          the most probable (preferred) action.
  
      Returns:
        Sampled point of shape dependent on the subclass and on the shape of
        inputs.
      """
      raise NotImplementedError
  
imports: import gin
  import gym
  import numpy
  from trax import layers
  from trax.fastmath import numpy
label_desc: Use image node_id 2 for calling the Distribution obj's underlying member method code with example usage: obj.sample(inputs, temperature) without return types